This simple approach has led to impressive results, which are state-of-the-art in many tasks such as detection [1]}, [2]}, segmentation [3]} and action recognition [4]}. Despite this success, we point out that it is difficult to disentangle the benefits offered by such a large-scale curated label dataset from the limitations of this pre-training paradigm.
Putting aside the discussion on the collection effort (cost, requiring in-domain expertise, etc), we point out that pre-training a model on a dataset and fine-tuning it on another can introduce two sort of discrepancies.
